Today's widely used indicators attempt to measure a country's economic performance but their ability to convey useful information and trends is less than optimal. Normally these indicators incorporate a number of economic variables and are based on complicated econometric procedures that render them too complex to be of much value to the general public, the media, or even many public policy makers.
Secondly, most of the indicators measure business cycles, not the general state of the economy. GDP or GDP per capita is probably the most widely accepted indicator for measuring economic welfare in theory and practice but it provides only a limited snapshot of the economy. In addition to individual indicators, the National Bureau of Economic Research (NBER) and the Conference Board calculate composite indexes. These economic indexes are based on Stock and Watson's methodology (James H. Stock and Mark W. Watson, “New Indexes of Coincident and Learning Economic Indicators,” NBER Macroeconomics Annual 1989, The MIT Press (1989), pp. 351-394) and are divided into leading (LEI), lagging, and coincident (CEI) economic indicators.
The first set of leading indicators was developed in the 1930's by Wesley Mitchell, Arthur Burns and their colleagues from NBER. Today, this composite index of leading indicators is widely accepted as a guide to predicting future economic activity (See technical discussion of indexes construction in Handbook of Economic Forecasting, Volume 1, pp. 1-1012 (2006), G. Elliott, C. W. J. Granger and A. Timmermann, eds., especially Chapter 16, “Leading Indicators” by Massimiliano Marcellino (pp. 879-960) and Chapter 17, “Forecasting with Real-Time Macroeconomic Data” by Dean Croushore (pp. 961-982)). Nevertheless, it has been strongly criticized for its lack of theoretical underpinning. The most commonly used leading economic indexes are The Conference Board Leading Economic Index™ (LEI) and the Conference Board Coincident Economic Index™ (CEI).
CEI is a weighted sum of four indicators including:
(1) employees on nonagricultural payrolls;
(2) personal income less transfer payments;
(3) industrial production; plus
(4) manufacturing and trade sales.
The LEI is a weighted sum of ten indicators including:
(1) average weekly hours, manufacturing;
(2) average weekly initial claims for unemployment insurance;
(3) manufacturers' new orders, consumer goods and materials;
(4) index of supplier deliveries-vendor performance;
(5) manufacturers' new orders, non-defense capital goods;
(6) building permits, new private housing units;
(7) stock prices, the Standard & Poor's 500 stock index;
(8) the Money supply (M2);
(9) interest rate spread, 10-year Treasury bonds less federal funds rate;
(10) Index of consumer expectations.
The variables were chosen to maximize the predictability of the indexes using complicated econometric procedures.
The most direct successor of the Stock and Watson indexes is the Chicago-Fed National Activity Index (CNFAI) which is a monthly index constructed from 85 indicators based on an extension of the methodology used to construct the original Stock-Watson coincident index.
Criticisms of the pioneering paper of Mitchell and Burns (1938) start with Tjalling Koopmans's paper, “Measurement Without Theory” (1947), which argues that there is no underlying theoretical basis for the inclusion, exclusion, or classification of measures which “limits the value . . . of the results obtained or obtainable.” Marcellino adds:                [ . . . ] leading indexes [ . . . ] are subject to several criticisms. For example, there is no explicit reference to the target variable in the construction of the composite leading index and the weighting scheme is fixed over time, with periodic revisions mostly due either to data issues, such as changes in the production process of an indicator or to the past unsatisfactory performance of the index. (Chapter 16 “Leading Indicators,” p. 882)        
The primary aim of such indicators is to reveal and predict business cycles, not to compare the general state of the economy at different times. But even in this case, leading indicators often fail due to structural changes in the economy. Diebold and Rosebush (1991a, 1991b) put together a real-time data set on the leading indicators and came to the conclusion that “the index of leading indicators does not lead and it does not indicate!”
There are a number of other partial economic indicators that attempt to add social costs, environmental damage, income distribution, GDP growth, health, etc., such as the Index of Sustainable Economic Welfare (ISEW), the Genuine Progress Indicator (GPE) and the Happy Planet Index (HPI). Like CEI and LEI, however, all of these are difficult to interpret.
Despite recent advancements in the science of economics, many individuals remain uneducated in basic economic theory and confused by the vast array of economic statistics reported by the media. Furthermore, many people are unable to properly assess their country's current economic performance and contrast it with its past performance; that is: they cannot place current performance within any historical context. These problems arise from a number of factors including:                the sheer number of economic statistics used by business and government, their complexity and reporting biases by the media;        a lack of historical context necessary to convey statistical and economic trends; and        a lack of context vis-à-vis other statistics (i.e., not all statistics are created equal with some clearly more important and meaningful than others).        
As a result, important information regarding economic performance is lost on the public. For example, many individuals are unable to identify whether it is a good time to solicit an increase in wages, undertake a major expenditure such as an automobile, invest in real assets such as a new or larger home, make changes to the asset allocation of investments or facilitate changes to retirement savings. Businesses also suffer uncertainty when investing in new projects or making important decisions regarding the efficient allocation of capital and labor.
In the political arena, politicians, policy advisors, and even the experts who advise them lack the ability to properly assess current macroeconomic performance. That is, how is the economy performing relative to last month, last year, or a previous generation? What is our economy's performance relative to our trading partners? Are current economic policies working as desired or simply targeting some issue de jour at the expense of the larger macro economy and the general population as a whole? Worst of all, voters are confronted with confusion and uncertainty. Many rely on an ad hoc set of metrics, the media or politicians themselves to explain the economy's performance. Unfortunately, the news media often have political biases and politicians have little incentive to educate voters on actual economic performance and instead manipulate economic information for political advantage.